Abstract
Smart beta strategies across economic regimes seek to address inefficiencies created by market-based indices, thereby enhancing portfolio returns above traditional benchmarks. Our goal is to develop a strategy for re-hedging smart beta portfolios that shows the connection between multi-factor strategies and macroeconomic variables. This is done, first, by analyzing finite correlations between the portfolio weights and macroeconomic variables and, more remarkably, by defining an investment tilting variable. The latter is analyzed with a discriminant analysis approach with a twofold application. The first is the selection of the crucial re-hedging thresholds which generate a strong connection between factors and macroeconomic variables. The second is forecasting portfolio dynamics (gain and loss). The capability of forecasting is even more evident in the COVID-19 period. Analysis is carried out on the iShares US exchange traded fund (ETF) market using monthly data in the period December 2013–May 2020, thereby highlighting the impact of COVID-19.
Highlights
A recent survey conducted by FTSE Russell Smart Beta Survey (2016) highlights that a wide range of institutional investors are increasingly implementing smart beta portfolio strategies as part of their active equity allocation
We provide some evidence that portfolio dynamics, namely weight dynamics, are a good tool to reveal the link between smart beta strategies (“smart betas” for short) and macroeconomic variables
We start by motivating our choice of the macroeconomic variables gross domestic product (GDP), consumer price index (CPI), and FED and the fear index volatility index (VIX)
Summary
A recent survey conducted by FTSE Russell Smart Beta Survey (2016) highlights that a wide range of institutional investors are increasingly implementing smart beta portfolio strategies as part of their active equity allocation. Smart beta strategies emphasize the use of index construction rules alternative to traditional market capitalization-based indices through a factor-investing framework and considering diversification to avoid facing unrewarded risks (idiosyncratic risk), according to the meaning of smart beta “2.0” (Amenc et al 2014). Smart beta “1.0” aims to provide superior risk-adjusted performance compared to market capitalization weighted indices, but it generally cannot overcome drawbacks in the latter: tilt toward unrewarded risk and excess of concentration (Autier et al 2016). Smart beta strategies are a fair compromise between “passive” and “active” strategies: “passive” in the sense that they are exchange traded funds (ETFs) that aim to replicate benchmarks and “active” since they permit exposure to rewarded risk factors to be managed differently from a market capitalization-based index
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